Here we compare the baseline model trained with ID [1]} with our proposed method ConCURL. We randomly sample 10 and 15 classes from 1000 class ImageNet and evaluate the clustering accuracy on train split of the data using model trained from original ImageNet-10 and ImageNet-Dogs . We repeat the process 100 times for both 10 classes and 15 classes dataset and call it a random-10 and random-15 dataset respectively. Note that we don't retrain the model on the randomly sampled dataset but we only evaluate on it. We show the histogram of the accuracies we got in these 100 random datasets. In figure REF , we compare accuracy on both random-10 and random-15 for ConCURL model and baseline ID model trained on ImageNet-10 . Along with histogram we show Gaussian distribution (in red dotted line) with first and second moment equal to average and standard deviation of of all accuracies respectively. Similarly in figure REF  we show accuracies based on models trained on ImageNet-Dogs . For models trained on ImageNet-10 , baseline ID model performs slightly better compared to the proposed ConCURL model. The trend is reversed for the evaluation based on model trained on ImageNet-Dogs where ConCURL performs better compared to the baseline model. Even though proposed method performs best in max-performance it performs slightly worse on random-10. This result strengthens our argument of the need of going beyond traditional reporting of max-performance based on Acc, NMI and ARI.
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